Configuration management for a shared pool of configurable computing resources

ABSTRACT

Disclosed aspects manage a shared pool of configurable computing resources. A set of scaling factor data is monitored. The set of scaling factor data is related to a workload on a configuration of the shared pool of configurable computing resources. A set of workload resource data associated with the workload is ascertained. Using the set of scaling factor data and the set of workload resource data, a triggering event is detected. In response to detecting the triggering event, a configuration action (with respect to the configuration of the shared pool of configurable computing resources) is performed.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to managing a shared pool of configurablecomputing resources. The amount of data that needs to be managed byenterprises is increasing. Management of a shared pool of configurablecomputing resources may be desired to be performed as efficiently aspossible. As data needing to be managed increases, the need formanagement efficiency may increase.

SUMMARY

Aspects of the disclosure can monitor a set of scaling factor data andautomatically/dynamically resize one or more virtual machines.Virtualization/cloud software may be coupled for maintaining/managingvirtual machines on hosts with scaling factors indicating resourcerequirements for features such as hardware components. Accordingly, acloud environment configuration/arrangement may be dynamicallyreconfigured/rearranged. Such configuration actions may occur in anongoing basis by monitoring scaling factors and by usingmanagement/optimization techniques with respect to the scalingfactors/resource requirements.

Aspects of the disclosure include managing a shared pool of configurablecomputing resources. A set of scaling factor data is monitored. The setof scaling factor data is related to a workload on a configuration ofthe shared pool of configurable computing resources. A set of workloadresource data associated with the workload is ascertained. Using the setof scaling factor data and the set of workload resource data, atriggering event is detected. In response to detecting the triggeringevent, a configuration action (with respect to the configuration of theshared pool of configurable computing resources) is performed.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a cloud computing node according to embodiments.

FIG. 2 depicts a cloud computing environment according to embodiments.

FIG. 3 depicts abstraction model layers according to embodiments.

FIG. 4 is a flowchart illustrating a method of managing a shared pool ofconfigurable computing resources according to embodiments.

FIG. 5 shows an example system for managing a shared pool ofconfigurable computing resources according to embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure can monitor a set of scaling factor data andautomatically/dynamically resize one or more virtual machines.Virtualization/cloud software may be coupled for maintaining/managingvirtual machines on hosts with scaling factors indicating resourcerequirements for features such as hardware components. Accordingly, acloud environment configuration/arrangement may be dynamicallyreconfigured/rearranged (e.g., without utilizing user intervention whenreconfiguring/rearranging). Such configuration actions may occur in anongoing basis by monitoring scaling factors (e.g., in addition to orinstead of monitoring memory usage or processor utilization) and byusing management/optimization techniques (e.g., avoiding frequentresizes which can burden availability due to rebooting of virtualmachines) with respect to the scaling factors/resource requirements.

Cloud management software may use templates/flavors to define a limitedset of predefined resource configurations to ease in specifying the sizeof a virtual machine. It may then be the responsibility of the cloudadministrator to work-out a portion of these templates for the varioustypes of workloads to be run-on. Users may be faced with adjusting thevalues for individual virtual machines based on their own understandingof the workload and scale they intend to run. This can result in theusers making decisions in areas they have little experience or inimproperly sized virtual machines. As such, neither the cloudadministrator nor cloud user may have appropriate information toefficiently manage resource requirements for the virtual machines for agiven scale. Disclosed aspects may provide performance or efficiencybenefits with respect to this characteristic.

Aspects of the disclosure include a method, system, and computer programproduct of managing a shared pool of configurable computing resources. Aset of scaling factor data (e.g., transactions per day) is monitored.The set of scaling factor data is related to a workload (e.g., aninstantiation of one or more images) on a configuration of the sharedpool of configurable computing resources. A set of workload resourcedata associated with the workload is ascertained. Using the set ofscaling factor data and the set of workload resource data, a triggeringevent is detected. In response to detecting the triggering event, aconfiguration action (with respect to the configuration of the sharedpool of configurable computing resources) is performed.

In embodiments, the set of scaling factor data related to the workloadis collected. The set of scaling factor data can include at least one ofa set of transaction processing scaling factor data, a set of useraccess scaling factor data, a set of entity storage scaling factor data,a set of product usage scaling factor data, or a set of provider-definedscaling factor data. In embodiments, detecting the triggering eventincludes identifying that a first value of a parameter of the set ofscaling factor data differs (e.g., exceeds a threshold difference) withrespect to a second value of the parameter of the set of workloadresource data.

In embodiments, the configuration action includes modifying theconfiguration of the shared pool of configurable computing resources.Modifying the configuration can include changing a configuration of atleast one of a set of processing resources, a set of memory resources, aset of disk resources, or a set of virtual machines. Altogether,performance or efficiency benefits when managing a shared pool ofconfigurable computing resources may occur (e.g., speed, flexibility,load balancing, responsiveness, resource usage, productivity). Aspectsmay save resources such as bandwidth, processing, or memory.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, tabletcomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processing unit 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media. An example ofremovable media is shown in FIG. 1 to include a Digital Video Disc (DVD)192.

System memory 130 can include computer system readable media in the formof volatile or non-volatile memory, such as firmware 132. Firmware 132provides an interface to the hardware of computer system/server 110.System memory 130 can also include computer system readable media in theform of volatile memory, such as random access memory (RAM) 134 and/orcache memory 136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, Redundant Array of Independent Disk(RAID) systems, tape drives, data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 in FIG. 2 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM System z systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM System p systems; IBMSystem x systems; IBM BladeCenter systems; storage devices; networks andnetworking components. Examples of software components include networkapplication server software, in one example IBM WebSphere® applicationserver software; and database software, in one example IBM DB2® databasesoftware. IBM, System z, System p, System x, BladeCenter, WebSphere, andDB2 are trademarks of International Business Machines Corporationregistered in many jurisdictions worldwide.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. A cloud manager 350 is representative of a cloudmanager (or shared pool manager) as described in more detail below.While the cloud manager 350 is shown in FIG. 3 to reside in themanagement layer 330, cloud manager 350 can span all of the levels shownin FIG. 3, as discussed below.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and a configuration action 360, which may be used asdiscussed in more detail below.

FIG. 4 is a flowchart illustrating a method 400 of managing a sharedpool of configurable computing resources according to embodiments. Theshared pool of configurable computing resources may utilize a sharedpool manager (e.g., a controller, a cloud manager) to execute/carry-outprocesses/tasks. For example, virtual machines in a cloud environment(e.g., public, private, hybrid) can be maintained, managed, configured,reconfigured, arranged, or rearranged utilizing resource requirements(e.g., number of virtual processors, amount of memory, disk space) forthe virtual machines based on requirements for the workload and thescale which it is run. The shared pool manager may or may not beincluded in the shared pool of configurable computing resources. Method400 may begin at block 401.

In embodiments, a set of scaling factor data related to a workload maybe collected at block 410. Collection can include gathering currentscaling factor values for the workload. Collection may be performedusing a pull technique (e.g., retrieving the set of scaling factor datafrom the workload), a publish-subscription technique (e.g., the workloadpushes-out the set of scaling factor data), or the like. Anauthentication key or credential may be registered/used to authorizecollection of the set of scaling factor data (e.g., for the workload toallow/grant access/permission).

In various embodiments, collection of the set of scaling factor data mayoccur on a polling schedule (e.g., at a frequency of a temporal periodat block 416) or based on an event (e.g., in response to a usage eventat block 418). To illustrate, in response to collecting the set ofscaling factor data related to the workload, an operation may wait for atemporal period (e.g., 0 minutes, 10 minutes, 24 hours). In response towaiting for the temporal period, the set of scaling factor data relatedto the workload may be collected (again). Implementing a collectionfrequency, as such, can deter configuration modifications forshort-lived changes in the set of scaling factor data, scaling factors,scaling factor values, or the like. Similarly, collection may occur inresponse to a usage event related to the workload such as a usage changeexceeding a threshold with respect to the set of scaling factor data,scaling factors, scaling factor values, or the like (e.g., transactionprocessing by the workload decreases by 35%, number of users accessingthe workload increases by 15%).

At block 420, a set of scaling factor data is monitored. For instance,monitoring can include querying (e.g., asking a question), searching(e.g., exploring for a reason), obtaining (e.g., recording acollection), probing (e.g., checking a property), scanning (e.g.,reviewing a sample), or tracking (e.g., following a characteristic). Toillustrate, the set of scaling factor data (e.g., which influencesresource requirements) can include the number of transactions beingprocessed, the number of users accessing the system, the number ofentities being stored, etc. In embodiments, the set of scaling factordata can include a set of transaction processing scaling factor data 421(e.g., number of transactions), a set of user access scaling factor data422 (e.g., number of users), a set of entity storage scaling factor data423 (e.g., number of objects for storage), a set of product usagescaling factor data 424 (e.g., number of entities beingaccessed/managed), or a set of provider-defined scaling factor data 425(e.g., target scaling goals for the workload). For example, the set oftransaction processing scaling factor data 421 may include 500 totaltransactions, 400 transactions per day, or twelve-hours of transactions.For instance, the set of user access scaling factor data 422 may include100 total users, 45 current/active users, or 16 user-groups. Otherpossibilities related to the set of scaling factor data are consideredand contemplated.

The set of scaling factor data is related to a workload on aconfiguration of the shared pool of configurable computing resources.Accordingly, the set of scaling factor data may indicate historical,current/actual, or expected/predicted asset/resource usage for theworkload. The workload may include an instantiation of one or moreimages (e.g., a type/version of software configured to run) at block427. The workload can include an active workload (e.g., a runningworkload, an available workload, an online workload) at block 428. Assuch, the set of scaling factor data may be collected/monitored withrespect to the workload in a dynamic manner (e.g., in real-time whilethe workload is processing/operating). In embodiments, the configurationincludes at least one of a configuration/arrangement of a set ofprocessing resources at block 431 (e.g., 5 type A processors), aconfiguration/arrangement of a set of memory resources at block 432(e.g., 2 gigabytes of type D memory), a configuration/arrangement of aset of disk resources at block 433 (e.g., 8 type H disk drives having500 total gigabytes of storage), or a configuration/arrangement of a setof virtual machines at block 434.

At block 440, a set of workload resource data associated with theworkload is ascertained. Ascertaining (e.g., finding-out, discovering,identifying) the set of workload resource data associated with theworkload can include retrieving the set of workload resource data (e.g.,from a registry). The set of workload resource data may includeinformation received from a workload provider (e.g., company whodesigned software to execute such workloads). For example,applications/virtual appliance vendors may provide the set of workloadresource data (e.g., information in the form of documentation or sizingtools) to be used with respect to resource requirements given some setof scaling factor values. In various embodiments, the set of workloadresource data may indicate or map-to one or more configurations (e.g.,based on one or more parameters/inputs/scaling factors). For example,the registry may store mappings of ranges to requirements (e.g., 1-500transactions maps to 1 processor and 4 gigabytes of memory, 501-1000transactions maps to 2 processors and 8 gigabytes of memory).

In certain embodiments, the set of workload data may be included/storedin a registry (e.g., a database, as part of the shared pool ofconfigurable computing resources). For example, the registry can providea programmatic interface for a supplier to register workload identifiersand supply their resource requirements based on scaling factors. Also,the registry can include the set of scaling factor data for a givenworkload and compute the resource requirements for the given workload.The registry may include a public registry (e.g., a publicly availabledatabase of templates/flavors defining one or more resourceconfigurations), a nonpublic registry (e.g., a privately held databaseof templates/flavors defining one or more resource configurations), or ahybrid (e.g., combination of public/nonpublic).

At block 460, a triggering event is detected using the set of scalingfactor data and the set of workload resource data. In general, thetriggering event may occur when scaling factors change. For example, theset of scaling factor data and the set of workload resource data may becompared. If the resource requirements have changed from previousresource requirements, the triggering event may occur (and be detected).For example, scaling factor values may have initially had 200transactions per hour with 15 users having access. Such values may beassociated with 2 type B processors and 1 gigabyte of type F memory. Inrunning the workload, 400 transactions may be being carried-out per hourwith 30 users actually accessing. Under those circumstances, typically 4type B processors and 1.5 gigabytes of type E may would be called-for.As such, the triggering event may be detected.

In embodiments, detecting the triggering event (using the set of scalingfactor data and the set of workload resource data) includes identifyingthat a first value of a parameter of the set of scaling factor datadiffers with respect to a second value of the parameter of the set ofworkload resource data at block 465. For example, 400 transactions perhour (e.g., actual/current value) in the set of scaling factor datadiffers from 200 transactions per hour (e.g., initial/expected value) inthe set of workload resource data. In various embodiments, by comparingthe first value with the second value and using a threshold differencevalue, it may be computed that the threshold difference value isexceeded at block 466. For instance, the threshold difference value maybe 100 transactions per hour. Since the first and second values differby 200 transactions per hour (400−200=200), the threshold difference isexceeded. In certain embodiments, detecting the triggering event (usingthe set of scaling factor data and the set of workload resource data)includes identifying a trend in the set of scaling factor data at block469. To illustrate, over time the amount of memory being used increaseson an hour-over-hour basis. Thus, the trend of increasing memory canfulfill the triggering event (e.g., indicating potential appropriatenessof a configuration action).

At block 480, a configuration action is performed. The configurationaction occurs with respect to the configuration of the shared pool ofconfigurable computing resources. The configuration action is performedin response to detecting the triggering event. In embodiments, theconfiguration action includes modifying the configuration of the sharedpool of configurable computing resources at block 481. The configurationmay be (dynamically) computed to resolve a flavor which specifies a sizeof a virtual machine for a type of workload and scale intended to berun/executed/processed. Accordingly, the configuration can be modifiedto process the workload (e.g., using one or more resized virtualmachines). In certain embodiments, the configuration may be determinedby resolving a set of resource attributes which indicates theconfiguration. Resolving the set of resource attributes can includedetermining/computing processing, memory, or disk types/features. Forinstance, various algorithms may be utilized which account for the setof scaling factor data. For certain workloads, a particular resourceattribute such as memory may be prioritized with respect to another suchprocessing power. For example, the set of resource attributes may bepulled from the registry to formulate an appropriate configuration.

For example, for a virtual machine resize operation, the shared poolmanager may associate a virtual machine image (e.g., using the set ofscaling factor data and other workload properties) with a workloadidentifier indicated in the set of workload data. The workloadidentifier can be associated with a configuration template/flavor andspecified on the resize interfaces. To illustrate, the set of scalingfactor data may include 950 expected transactions per day which can beaccessed by 35 users. The set of workload data may indicate that for 900to 1000 expected transactions per day which can be accessed by 20 to 50users the appropriate configuration is identified as the flavor “2P-8M”which includes two processors and eight gigabytes of memory. In variousembodiments, a workload estimator tool may be utilized which can defineresource requirements or partition configurations.

In certain embodiments, a workload identifier is coupled with aconfiguration flavor (e.g., to form a couplet in a data store). Theworkload identifier can include a unique name, numeric, or otheridentifier for the workload within the shared pool or more globally. Theconfiguration flavor can include an arrangement, organization, design,feature, component, or other aspect of one or more resources. Theworkload identifier and the configuration flavor may becoupled/connected/linked for efficiency in processing the determinationof the configuration to be used. For example, certain couplets may bepredetermined or computed in advance (e.g., the workload provided mayhave selected a configuration flavor based on how the workload wasdesigned or for its intended usage). Other couplets may be based onmachine learning or historical usage.

In various example embodiments, affinity/anti-affinity propertiesreturned from a registry query for a workload identifier can returnrules referencing other workload identifiers. A shared pool manager mayuse these rules to define its policies and filters in its placementarrangement logic. In certain example embodiments, storage typesreturned from the registry for a workload identifier can returninformation such as the required storage attachment technology (e.g.N-Port ID Virtualization (NPIV), Internet Small Computer SystemInterface (iSCSI)), number of paths to storage for redundancy andthroughput, or Input/Output Operations Per Second (IOPS).

In various embodiments, in response to modifying the configuration ofthe shared pool of configurable computing resources, use of theconfiguration of the shared pool of configurable computing resources ismetered at block 483. For example, the configuration may be measuredbased on factors such as quantity of assets configured, temporal periodsof configuration/arrangement, actual usage of assets, available usage ofassets, etc. Such factors may correlate to charge-back or cost burdenswhich can be defined in-advance (e.g., utilizing usage tiers) or scaledwith respect to a market-rate. An invoice or bill presenting the usage,rendered services, fee, and other payment terms may be generated basedon the metered use at block 484. The generated invoice may be provided(e.g., displayed in a dialog box, sent or transferred by e-mail, textmessage, traditional mail) to the user for notification, acknowledgment,or payment.

In certain embodiments, the configuration action can include anotification at block 489. For example, a notification may be providedregarding the configuration of the shared pool of configurable computingresources. In response to providing the notification, components maywait for a temporal period (e.g., 0 seconds, 2 minutes, 1 hours, 2days). In response to waiting for the temporal period, the configurationof the shared pool of configurable computing resources may be modified.

Method 400 concludes at block 499. Aspects of method 400 may provideperformance or efficiency benefits for managing a shared pool ofconfigurable computing resources. For example, aspects of method 400 mayhave positive impacts with respect to resizing one or more virtualmachines (e.g., properties, placement arrangement). Altogether,performance or efficiency benefits when managing a shared pool ofconfigurable computing resources may occur (e.g., speed, load balancing,flexibility, responsiveness, resource usage, productivity).

FIG. 5 shows an example system 500 for managing a shared pool ofconfigurable computing resources according to embodiments. Inembodiments, method 400 may be implemented within aspects of the examplesystem 500 (e.g., within cloud environment 550). Components, features,or elements depicted in FIG. 6 need not be present, utilized, or locatedas such in every such similar system, and such components are presentedas an illustrative example. Aspects of example system 500 may beimplemented in hardware, software or firmware executable on hardware, ora combination thereof.

The shared pool of configurable computing resources (e.g., cloudenvironment 550) can include (but need not include) a shared poolmanager 523, a registry 521, a resizer 529, a monitor 525, a collector524, and a virtual machine 527 having a workload 528. One or morevirtual machines can cooperate to provide a computing capability (e.g.,store data using memory, process data using a processor). Workloadprovider 520, cloud management user 526, and cloud managementadministrator 522 may also be included in the example system 500.Disclosed aspects may associate a workload identifier with a virtualmachine image in order to retrieve a set of scaling factor data (e.g.,scaling factors and other workload properties) from the registry 521(e.g., a workload resource requirements registry). Examples of scalingfactors include number of transactions per hour, number of users, numberof products, etc. Examples of other properties includeaffinity/anti-affinity rules and storage types. Of course, examplesystem 500 could include many other features or other functions known inthe art which are not shown in FIG. 5.

Aspects of example system 500 includes a Scaling Factor Monitorframework which includes the monitor 525, the collector 524, and theresizer 529. The Scaling Factor Monitor framework can prescribeprogrammable collection interfaces that when implemented, collect a setof scaling factor data (e.g., the current scaling factor values) for theworkload 528. The collection interfaces could be implemented within oneor more components making up the workload 528 or a separate piece ofsoftware that interrogates the workload 528 to ascertain/determine theset of scaling factor data (e.g., a third-party agent reads statisticswhich the workload software provides and implements the collectioninterfaces). The collector 524 may include an application program orsoftware that implements the collection interfaces. The collector 524may register credentials or a collection frequency with the monitor 525.The collection frequency can be configured to avoid resizes fortemporary or short-lived changes in scaling factors.

The monitor 525 can retrieve/receive the set of scaling factor data orworkload resource data for the workload 528 by using various interfaces.Using this information and credentials registered by the collector 524,the monitor 525 can periodically (e.g., based on a frequency value)invoke the collection interfaces to the collector 524 toretrieve/receive the set of scaling factor data (e.g., current scalingfactor values) and may use the returned values to invoke a sizing logicto determine if a resize is required or may be beneficial. If so, itinvokes the resizer 529. The resizer 529 can implement a set ofinterfaces that the Scaling Factor Monitor framework defines to initiateresize operations. The resizer 529 may use cloud management interfacesto perform a resize or send a notification to the cloud managementadministrator 522 to perform the resize.

FIG. 5 includes an example set of flow operations (some of which may beconfigured to repeat according to embodiments). At flow operation 501,the workload provider 520 may register a workload identifier, itsscaling factors, and resource requirements based on scaling factors. Atflow operation 502, the cloud management administrator 522 can registersworkload identifier with a flavor. At flow operation 503, the cloudmanagement administrator 522 may register credentials and frequency forthe workload identifier with the collector 524. At flow operation 504,the collector 524 can register credentials and collection frequency forthe workload identifier with monitor 525. At flow operation 505, themonitor 525 may get scaling factors for the workload identifier from theregistry 521.

At flow operation 506, the cloud management user 526 may request tocreate a virtual machine for a flavor having a specific workload. Atflow operation 507, the shared pool manager 507 can retrieve/receivescaling factors for the workload from the registry 521. At flowoperation 508, the shared pool manager 523 may get values for thescaling factors from the cloud management user 526. At flow operation509, the shared pool manager 523 can retrieve/receive resourcerequirements from the registry 521 given the scaling factor values andthe workload. At flow operation 510, the shared pool manager 523 may usereturned resource requirements in establishing an appropriately sizedvirtual machine 527.

At flow operation 511, a frequency may be registered and, usingcredentials for the workload and scaling factors, the monitor 525 canrequest the set of scaling factor data (e.g., scaling factor values)from the collector 524 for the workload 528. At flow operation 512, thecollector may interface to the workload 528 in the virtual machine 527to get the set of scaling factor data (in embodiments, the collector maybe collecting these values on a separate polling schedule or viaevent-based methods). At flow operation 513, the monitor 525 may requestresource requirements from the registry given the set of scaling factordata (e.g., the current scaling factor values). At flow operation 514,if the resource requirements have changed from previous resourcerequirements for the virtual machine 527, the monitor 525 may invoke theresizer 529 to initiate resizing the virtual machine 527. At flowoperation 515, the resizer 529 may invoke the shared pool manager 523 torequest the virtual machine 527 to be resized. At flow operation 516,the shared pool manager 523 can perform the virtual machine resize (orsend a notification to the cloud management administrator 522 to performthe resize).

Aspects of example system 500 may provide performance or efficiencybenefits when managing a shared pool of configurable computingresources. For example, aspects of system 600 may save resources such asbandwidth, processing, or memory (e.g., properly sized virtualmachines). Altogether, a shared pool of configurable computing resourcesmay be managed more efficiently.

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Themodules are listed and described illustratively according to anembodiment and are not meant to indicate necessity of a particularmodule or exclusivity of other potential modules (or functions/purposesas applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer-implemented method of managing a shared pool ofconfigurable computing resources, the method comprising: monitoring aset of scaling factor data related to an active workload on aconfiguration of the shared pool of configurable computing resources;ascertaining a set of workload resource data associated with the activeworkload; detecting, using the set of scaling factor data and the set ofworkload resource data, a triggering event; and performing, in responseto detecting the triggering event, a configuration action with respectto the configuration of the shared pool of configurable computingresources, wherein the configuration action includes: reconfiguring theconfiguration of the shared pool of configurable computing resources. 2.The method of claim 1, further comprising collecting the set of scalingfactor data related to the active workload.
 3. The method of claim 2,further comprising: waiting, in response to collecting the set ofscaling factor data related to the active workload, for a temporalperiod; and collecting, in response to waiting for the temporal period,the set of scaling factor data related to the active workload.
 4. Themethod of claim 2, wherein the set of scaling factor data related to theactive workload is collected in response to a usage event.
 5. The methodof claim 1, wherein the set of scaling factor data is selected from agroup consisting of: a set of transaction processing scaling factordata, a set of user access scaling factor data, a set of entity storagescaling factor data, a set of product usage scaling factor data, and aset of provider-defined scaling factor data which indicates one or moretarget scaling goals for the active workload.
 6. (canceled) 7.(canceled)
 8. The method of claim 1, wherein the configuration isselected from a group consisting of: a configuration of a set ofprocessing resources, a configuration of a set of memory resources, aconfiguration of a set of disk resources, and a configuration of a setof virtual machines.
 9. The method of claim 1, wherein detecting, usingthe set of scaling factor data and the set of workload resource data,the triggering event includes: identifying that a first value of aparameter of the set of scaling factor data differs with respect to asecond value of the parameter of the set of workload resource data. 10.The method of claim 9, further comprising: computing, by comparing thefirst value with the second value and using a threshold differencevalue, that the threshold difference value is exceeded.
 11. The methodof claim 1, wherein detecting, using the set of scaling factor data andthe set of workload resource data, the triggering event includes:identifying a trend in the set of scaling factor data. 12-21. (canceled)22. The method of claim 1, wherein ascertaining the set of workloadresource data associated with the active workload includes: retrieving,from a registry, the set of workload resource data indicated by aworkload provider.
 23. The method of claim 2, wherein collecting the setof scaling factor data related to the active workload includes:retrieving, using a pull technique, the set of scaling factor data froman online workload in real-time while the online workload is operating.24. The method of claim 4, wherein the usage event includes a usagechange exceeding a threshold with respect to the set of scaling factordata related to the active workload on the configuration of the sharedpool of configurable computing resources.
 25. The method of claim 10,further comprising: detecting, with respect to the active workload, achange in a set of transaction processing scaling factor data of the setof scaling factor data, wherein the first value of the parameter of theset of scaling factor data corresponds to a transaction processing rate.26-27. (canceled)
 28. The method of claim 1, wherein the configurationaction includes: waiting for a temporal period; and reconfiguring, inresponse to waiting for the temporal period, the configuration of theshared pool of configurable computing resources.
 29. The method of claim1, further comprising: resizing, to reconfigure the configuration of theshared pool of configurable computing resources, one or more virtualmachines.
 30. The method of claim 29, further comprising: monitoring, tomonitor the set of scaling factor data related to the active workload onthe configuration of the shared pool of configurable computingresources, a set of transaction processing scaling factor data; anddetecting, using the set of transaction processing scaling factor data,the triggering event.
 31. The method of claim 29, further comprising:monitoring, to monitor the set of scaling factor data related to theactive workload on the configuration of the shared pool of configurablecomputing resources, a set of user access scaling factor data; anddetecting, using the set of user access scaling factor data, thetriggering event.
 32. The method of claim 29, further comprising:monitoring, to monitor the set of scaling factor data related to theactive workload on the configuration of the shared pool of configurablecomputing resources, a set of entity storage scaling factor data; anddetecting, using the set of entity storage scaling factor data, thetriggering event.
 33. The method of claim 29, further comprising:monitoring, to monitor the set of scaling factor data related to theactive workload on the configuration of the shared pool of configurablecomputing resources, a set of product usage scaling factor data; anddetecting, using the set of product usage scaling factor data, thetriggering event.
 34. The method of claim 29, further comprising:monitoring, to monitor the set of scaling factor data related to theactive workload on the configuration of the shared pool of configurablecomputing resources, a set of provider-defined scaling factor data; anddetecting, using the set of provider-defined scaling factor data, thetriggering event.